Multiple description (MD) coding systems and techniques encode a source signal into two or more parts or “descriptions” each containing an amount of information that is sufficient to permit reconstruction of a lower quality version of the original source signal. Ideally, the decoder in a MD coding system can reconstruct a reasonable facsimile of the source signal from one or more of these descriptions but the fidelity of the reconstructed facsimile increases as the number of descriptions increases.
The basic idea behind MD coding systems is to divide an encoded signal into two or more descriptions so that each description represents a reasonable facsimile of the original source signal and so that each description shares some information with other descriptions. The decoder in a MD coding system gathers information from as many of these descriptions as possible, estimates the content of any missing descriptions from the information contained in the received descriptions, and reconstructs a facsimile of the source signal from the received and estimated descriptions.
MD coding techniques are attractive in a variety of applications where portions of an encoded signal may be lost or corrupted during transmission because they can provide a graceful degradation in the quality of a reconstructed facsimile as transmission-channel conditions become increasingly challenging. This characteristic is especially attractive for wireless packet networks that operate over relatively lossy transmission channels. Additional information about MD coding systems and techniques can be obtained from Goyal, “Multiple description coding: compression meets the network,” IEEE Signal Processing Magazine, September, 2001.
A number of MD techniques are known that may be used to divide encoded information into parts or descriptions. Some techniques apply a correlating transform to encoded information that distributes the information into two or more palts in a reversible or invertible way. Each part can be assembled into a separate bitstream or packet for storage or transmission. Unfortunately, known techniques for using correlating transforms can inject quantization noise into the encoded information that is divided into parts, which may degrade the perceived quality of the facsimile that is reconstructed by a decoder. Furthermore, known ways of implementing correlating transforms are computationally intensive, which requires a considerable amount of computational resources to perform the calculations needed for the transforms.
What is needed is a way to apply correlating transforms to encoded information that introduces little if any quantization noise and can be implemented efficiently.